Refined clothespin relocation test and assessment of motion

Background:Advancements in upper limb prosthesis design have focused on providing increased degrees of freedom for the end effector through multiple articulations of a prosthetic hand, wrist and elbow. Measuring improvement in patient function with these devices requires development of appropriate assessment tools.Objectives:This study presents a refined clothespin relocation test for measuring performance and assessing compensatory motion between able-bodied subjects and subjects with upper limb impairments.Study design:Comparative analysis.Methods:Trunk and head motions of 13 able-bodied subjects who performed the refined clothespin relocation test were compared to the motion of a transradial prosthesis user with a single degree of freedom hand.Results:There were observable differences between the prosthesis user and the able-bodied group. The assessment used provided a clear indication of the differences in motion through analysis of compensatory motion.Conclusion:The refined clothespin relocation test provides additional benefits over the standard clothespin assessment and makes identification of compensatory motions easily identifiable to the researcher. While this article establishes the method for the new assessment, further validation will need to be performed with more users.Clinical relevanceThe refined test provides a more defined structure for the trajectory of the hand/terminal device than the standard protocol for the clothespin relocation test. This will help researchers interested in motion studies of limb segments to efficiently compare and analyse motion between able-bodied and prosthesis user groups.

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Novel Design for a Simple and Affordable End-Effector for an Upper-Limb Prosthesis

International Journal for Service Learning in Engineering Humanitarian Engineering and Social Entrepreneurship ◽

10.24908/ijsle.v12i1.6691 ◽

2017 ◽

Vol 12 (1) ◽

pp. 62-73

Author(s):

Shawn Jackson ◽

Victoria Volk ◽

Jodi Prosise

Keyword(s):

Upper Limb ◽

Primary User ◽

Customer Requirements ◽

End Effector ◽

Total Cost ◽

Upper Limb Prosthesis ◽

Pvc Pipe ◽

End Effectors ◽

Novel Design ◽

Limb Prosthesis

An affordable prosthetic arm was designed to be used in underprivileged areas to allow more upper-limb amputees to have access to prosthetics that can help them regain their independence. The primary user of the prosthesis will be the amputee(s) who receive the device; beneficiaries include the amputee’s family and the community involved in the manufacturing of future prostheses. The customer requirements include cost-effectiveness, the ability to withstand humidity, cleanability, low maintenance, long life, lightweight, and the ability to perform simple tasks independently. A new end-effector was created that meets the range-of-motion required for the user to perform his/her required tasks. The end-effector is comprised of a PVC cap, PVC coupling, wooden ball holding the tool, and two wooden stop rings. The device screws onto the forearm PVC pipe and locks into place. Four end-effectors were created: toothbrush, fork, spoon, and pen. A change-out station was created to allow the user to independently attach and detach the different end-effectors. The total cost of the device was $20.66.

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Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use

PLoS ONE ◽

10.1371/journal.pone.0246795 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246795

Author(s):

Sophie L. Wang ◽

Conor Bloomer ◽

Gene Civillico ◽

Kimberly Kontson

Keyword(s):

Machine Learning ◽

Outcome Measures ◽

Upper Limb ◽

Degrees Of Freedom ◽

Clustering Algorithm ◽

The Body ◽

Movement Quality ◽

Upper Limb Prosthesis ◽

Norm Group ◽

Limb Prosthesis

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user’s ability to achieve normative movement.

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